Enhanced command selection in a networked computing environment

ABSTRACT

Embodiments of the present invention provide an approach for identifying commands for virtual resource instances in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a set of commands for an instance of a virtual resource may be received in a computer memory medium or the like. The commands may then be analyzed and information pertaining to the commands may be stored in a computer storage device or the like. When a user/requester later wishes to identify a command to be utilized for another instance of the virtual resource, the requester can access the information and be provided with a set of suggested commands that are typically utilized for similar and/or previous instances of the virtual resource.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit of, co-pending and co-owned U.S. patent application Ser. No. 13/875,464, filed May 2, 2013, the entire contents of which are herein incorporated by reference. This application is also related in some aspects to the commonly owned and issued U.S. Pat. No. 8,838,799, issued Sep. 16, 2014. This application is also related in some aspects to the commonly owned and co-pending U.S. patent application Ser. No. 14/330,043, filed Jul. 14, 2014.

TECHNICAL FIELD

In general, embodiments of the present invention provide an approach for virtual resource instance management. Specifically, embodiments of the present invention relate to an approach for identifying commands for virtual resource instances in a networked computing environment (e.g., a cloud computing environment).

BACKGROUND

The networked computing environment (e.g., cloud computing environment) is an enhancement to the predecessor grid environment, whereby multiple grids and other computation resources may be further enhanced by one or more additional abstraction layers (e.g., a cloud layer), thus making disparate devices appear to an end-consumer as a single pool of seamless resources. These resources may include such things as physical or logical computing engines, servers and devices, device memory, and storage devices, among others.

In networked computing environments, customers may instantiate instances of virtual images or other virtual resources. Challenges may exist, however, in identifying how and where to interact with a newly instantiated instance. Specifically, it may be difficult for a user/customer to select what commands to utilize with a particular instance.

SUMMARY

In general, embodiments of the present invention provide an approach for identifying commands for virtual resource instances in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a set of commands for an instance of a virtual resource may be received in a computer memory medium or the like. The commands may then be analyzed and information pertaining to the commands may be stored in a computer storage device or the like. When a user/requester later wishes to identify a command to be utilized for another instance of the virtual resource, the requester can access the information and be provided with a set of suggested commands that are typically utilized for similar and/or previous instances of the virtual resource.

A first aspect of the present invention provides a computer-implemented method for identifying commands for virtual resource instances in a networked computing environment, comprising: analyzing a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receiving, from a requester, a request to access at least a subset of the information; retrieving the at least a subset of the information from a computer storage device to generate a set of suggested commands; and providing the set of suggested commands to the requester.

A second aspect of the present invention provides a system for identifying commands for virtual resource instances in a networked computing environment, comprising: a memory medium comprising instructions; a bus coupled to the memory medium; and a processor coupled to the bus that when executing the instructions causes the system to: analyze a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receive, from a requester, a request to access at least a subset of the information; retrieve the at least a subset of the information from a computer storage device to generate a set of suggested commands; and provide the set of suggested commands to the requester.

A third aspect of the present invention provides a computer program product for identifying commands for virtual resource instances in a networked computing environment, the computer program product comprising a computer readable storage media, and program instructions stored on the computer readable storage media, to: analyze a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receive, from a requester, a request to access at least a subset of the information; retrieve the at least a subset of the information from a computer storage device to generate a set of suggested commands; and provide the set of suggested commands to the requester.

A fourth aspect of the present invention provides a method for deploying a system for identifying commands for virtual resource instances in a networked computing environment, comprising: providing a computer infrastructure being operable to: analyze a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receive, from a requester, a request to access at least a subset of the information; retrieve the at least a subset of the information from a computer storage device to generate a set of suggested commands; and provide the set of suggested commands to the requester.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readily understood from the following detailed description of the various aspects of the invention taken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing node according to an embodiment of the present invention;

FIG. 2 depicts a cloud computing environment according to an embodiment of the present invention;

FIG. 3 depicts abstraction model layers according to an embodiment of the present invention;

FIG. 4 depicts a system diagram according to an embodiment of the present invention;

FIG. 5 depicts a method flow diagram according to an embodiment of the present invention;

FIG. 6 depicts an example process flow according to an embodiment of the present invention;

FIG. 7 depicts a method flow diagram according to an embodiment of the present invention;

FIG. 8 depicts a method flow diagram according to an embodiment of the present invention;

FIG. 9 depicts a method flow diagram according to an embodiment of the present invention; and

FIG. 10 depicts a method flow diagram according to an embodiment of the present invention.

The drawings are not necessarily to scale. The drawings are merely schematic representations, not intended to portray specific parameters of the invention. The drawings are intended to depict only typical embodiments of the invention, and therefore should not be considered as limiting the scope of the invention. In the drawings, like numbering represents like elements.

DETAILED DESCRIPTION

Illustrative embodiments will now be described more fully herein with reference to the accompanying drawings, in which embodiments are shown. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this disclosure to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, the use of the terms “a”, “an”, etc., do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. The term “set” is intended to mean a quantity of at least one. It will be further understood that the terms “comprises” and/or “comprising”, or “includes” and/or “including”, when used in this specification, specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.

Embodiments of the present invention provide an approach for identifying commands for virtual resource instances in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a set of commands for an instance of a virtual resource may be received in a computer memory medium or the like. The commands may then be analyzed and information pertaining to the commands may be stored in a computer storage device or the like. When a user/requester later wishes to identify a command to be utilized for another instance of the virtual resource, the requester can access the information and be provided with a set of suggested commands that are typically utilized for similar and/or previous instances of the virtual resource.

It is understood in advance that although this disclosure includes a detailed description of cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

Characteristics are as follows. On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed, automatically without requiring human interaction with the service's provider.

Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.

Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active consumer accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited consumer-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application-hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computing node is shown. Cloud computing node 10 is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node 10 is capable of being implemented and/or performing any of the functionality set forth hereinabove.

In cloud computing node 10, there is a computer system/server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10 is shown in the form of a general-purpose computing device. The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

The embodiments of the invention may be implemented as a computer readable signal medium, which may include a propagated data signal with computer readable program code embodied therein (e.g., in baseband or as part of a carrier wave). Such a propagated signal may take any of a variety of forms including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium including, but not limited to, wireless, wireline, optical fiber cable, radio-frequency (RF), etc., or any suitable combination of the foregoing.

Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a consumer to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as private, community, public, or hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms, and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 2 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers provided by cloud computing environment 50 (FIG. 2) is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 3 are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframes. In one example, IBM® zSeries® systems and RISC (Reduced Instruction Set Computer) architecture based servers. In one example, IBM pSeries® systems, IBM System x® servers, IBM BladeCenter® systems, storage devices, networks, and networking components. Examples of software components include network application server software. In one example, IBM WebSphere® application server software and database software. In one example, IBM DB2® database software. (IBM, zSeries, pSeries, System x, BladeCenter, WebSphere, and DB2 are trademarks of International Business Machines Corporation registered in many jurisdictions worldwide.)

Virtualization layer 62 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions described below. Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and pricing provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. Consumer portal provides access to the cloud computing environment for consumers and system administrators. Service level management provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. Further shown in management layer is command identification, which represents the functionality that is provided under the embodiments of the present invention.

Workloads layer 66 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; transaction processing; and consumer data storage and backup. As mentioned above, all of the foregoing examples described with respect to FIG. 3 are illustrative only, and the invention is not limited to these examples.

It is understood that all functions of the present invention as described herein typically may be performed by the command identification functionality (of management layer 64, which can be tangibly embodied as modules of program code 42 of program/utility 40 (FIG. 1). However, this need not be the case. Rather, the functionality recited herein could be carried out/implemented and/or enabled by any of the layers 60-66 shown in FIG. 3.

It is reiterated that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, the embodiments of the present invention are intended to be implemented with any type of networked computing environment now known or later developed.

Referring now to FIG. 4, a system diagram describing the functionality discussed herein according to an embodiment of the present invention is shown. It is understood that the teachings recited herein may be practiced within any type of networked computing environment 86 (e.g., a cloud computing environment 50). A stand-alone computer system/server 12 is shown in FIG. 4 for illustrative purposes only. In the event the teachings recited herein are practiced in a networked computing environment 86, each client need not have a command identification engine (engine 70). Rather, engine 70 could be loaded on a server or server-capable device that communicates (e.g., wirelessly) with the clients to provide device protection therefor. Regardless, as depicted, engine 70 is shown within computer system/server 12. In general, engine 70 can be implemented as program/utility 40 on computer system 12 of FIG. 1 and can enable the functions recited herein. As further shown, engine 70 comprises a rules and/or computational engine that processes a set (at least one) of rules 78 and/or provides command identification hereunder. It is further understood that command identification engine 70 may be incorporated within or work in conjunction with any type of system that receives, processes and/or executes commands with respect to virtual resources in a networked computing environment. Such other system(s) have not been shown in FIG. 4 for brevity purposes.

Along these lines, engine 70 may perform multiple functions similar to a general-purpose computer. Specifically, among other functions, engine 70 may: receive, in a computer memory medium (e.g., 28 of FIG. 1), a set of commands (e.g., from users 88A-N) for an instance 74 of virtual resource 72 in the networked computing environment 86; analyze the set of commands to identify information 82A-N pertaining to the set of commands, the information 82A-N identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; store the information 82A-N in a computer storage device 80; execute at least one of the set of commands against the instance 74; receive, from a requester 90, a request to access at least a subset of the information; retrieve the at least a subset 84 of the information 82A-N from the computer storage device 80 to generate a set of suggested commands 95A-N; provide the set of suggested commands 95A-N to the requester 90; display the set of suggested commands 95A-N in a user interface accessible to the requester 90; display the at least the subset 84 of the information 82A-N within the virtual resource 72; refine the at least a subset 84 of the information 82A-N to generate the set of suggested commands 95A-N based on at least one of the following: a level of use of the set of commands, a time since the instance 74 was provisioned, a relative order of the set of commands, a common middleware installed across different images of the instance 74, newly installed middleware, and an installation topology of the common middleware and the newly installed middleware; and/or execute at least one of the set of suggested commands 95A-N on another instance 76 of the virtual resource 72.

ILLUSTRATIVE EXAMPLES

This section will describe illustrative examples of different scenarios in which the embodiments of the present invention can be carried out. It is understood in advance that the teachings recited herein are not intended to be limited to any particular scenario.

-   -   1. A cloud management provider (e.g., a company) implements         engine 70.     -   2. Individuals/companies using a cloud resource provided by the         cloud management provider can opt into transmitting command         information back to engine 70.         -   A. Preferences could be set such as:             -   1. Denied lists of commands not to share;             -   2. A threshold of uses before a command is shared (e.g.,                 only share after I've used a command 15 times);             -   3. Directories from which the command was executed to                 determine whether to share or not to share. (e.g., if                 from/home/private, don't share); and/or             -   4. Keywords such that it is permissible to share                 commands that include the word “start” or “shutdown.”     -   3. A user logs into the cloud resource and executes a command.     -   4. If enabled, the command is both executed against the cloud         resource and sent to the engine 70.     -   5. The engine 70 continually collects data such as:         -   A. Incoming commands;         -   B. The directory/path in which the command was executed;         -   C. The instance against which the command was executed;         -   D. Tags associated with the commands. (e.g., the user would             tag start-dfs.sh with “start Hadoop” so other users know             that the command starts Hadoop);         -   E. The geographical location where the command was executed;         -   F. A time instance 74 has been running;         -   G. An order of commands entered;         -   H. A success/failure measurement from excuting the commands;         -   I. Middleware/software of the system which the base image             had on them already—such information can be simply gathered             as image artifacts;         -   J. Newly installed middleware/software on the system which             were not part of installation from the base image—such             information can be gathered by agent scanning and passing             the information back to the cloud management system; and         -   K. Configuration and deployment (installation) topology of             those middleware/software. The system can see if one is in a             cluster environment and if so, primary, secondary, and so             on, including how many members in a cluster, etc. Such             information can be well defined by each middleware/software             for what information is appropriate to gather and helpful to             the system for cloud crowd command.     -   6. Optionally, highly simple commands (e.g., Is-al) may be         ignored by the system.     -   7. If another user wishes to view commands identified via crowd         sourcing they may do so at any time, e.g., those:         -   A. Displayed within a cloud management area associated with             each image; and/or         -   B. Displayed within the resource either:             -   1. From a pull mechanism;             -   2. As deployed as an artifact at instance creation time;             -   3. As a user begins to type a command; and/or             -   4. As a stand-alone asset associated with the resource.     -   8. Commands may be further refined based on:         -   A. Location of the user in comparison with location where             executed (e.g., view commands entered by other users located             in Dallas, Tex.);         -   B. Geographic location of the cloud user (e.g., other             individuals similar to you ran the following commands);         -   C. Popularity;         -   D. Keyword;         -   E. Expertise level of users executing the command;         -   F. A level of use of the set of commands;         -   G. A time since the instance was provisioned;         -   H. A relative order of the set of commands;         -   I. A common middleware installed across different images of             the instance;         -   J. Newly installed middleware; and         -   K. An installation topology of the common middleware and the             newly installed middleware.

These concepts will be further illustrated in conjunction with FIG. 5, which demonstrates additional logic (i.e., related artifacts) provided to the cloud crowd command information. That is, engine 70 (FIG. 4) allows a user to create “patterns” of components which correspond to collections of virtual machines, each performing a unique function and comprising a unique OS, middleware and software, etc. When such a pattern is used in cloud computing environment 50, there are often command line commands which are unique or closely related to that specific pattern. Engine 70 detects if a virtual machine exists as part of a pattern, and then associates all of the commands entered by a user to that pattern.

As shown in FIG. 5, engine 70 analyzes relationship artifacts to provide a process for enhanced command management in the cloud computing environment 50 (FIG. 4). In this embodiment, the following steps are performed:

-   -   A. A set of crowd cloud commands is gathered (i.e., step 501);     -   B. Additional information about the instance is gathered (i.e.,         step 502) such as:         -   1. Middleware/software of the system which the base image             had on them already—such information can be simply gathered             as image artifacts;         -   2. Newly installed middleware/software on the system which             were not part of installation from the base image—such             information can be gathered by agent scanning and passing             those information back to the cloud management system; and         -   3. Configuration and deployment (installation) topology of             the middleware/software. The system can see if one is in a             cluster environment and if so, primary, secondary, and so             on, including how many members in a cluster. Such             information can be well defined by each middleware/software             for what information is appropriate to gather and helpful to             the system for cloud crowd command;     -   C. Display the most popular or frequent commands executed         against an instance, including the order of the commands entered         (i.e., step 503);     -   D. Observe a state of the instance by its role in the system         (e.g., such as member of cluster, or primary for database or         service, etc.) (i.e., step 504);     -   E. Perform an analysis of all commands run by instances (i.e.,         step 505);     -   F. Group the commands by middleware/software, as well as by role         in the system (i.e., step 506);     -   G. Display the most relevant command run by other instances with         same artifacts when a user selects an instance in question and         asks for a common crowd command (i.e., step 507); and     -   H. Display a set of filters according to types of installations         and configurations to show only commands relevant to the         particular role of that installation, such as a cluster member.         The engine can suggest the right set of filters or the user can         choose the filter that they prefer to see (i.e., step 508).

This approach is beneficial because commands could be vastly different, e.g., while configuring a Websphere node based on the relationships that the Websphere node has with other nodes (artifacts). If a particular Websphere node is part of a cluster (e.g., Websphere XD) the steps will be different than if that node is isolated. Additional relationships will imply a different set of commands: Websphere->DB/2 vs Websphere->MySQL could have some effects as well. Therefore, engine 70 is configured to analyze the deployed pattern and further narrow down the recommended commands that a user might need. Engine 70 may suggest commands based on common middleware installed across different images. This could be enabled using “tagging” to apply a term to a command after it's executed for sharing purposes. Engine 70 could tag with the middleware, ex. DB2, and then all images that have DB2 installed might show that command regardless of how often it is used by others having an instance of the image.

Consider the process flow 600 shown in FIG. 6, wherein a user may select an instance with DB2 installation in which the DB2 has been configured to be a primary with another secondary node in a different instance. In this case, the user may want to see how the DB2 primary can manually sync data with the secondary node. Engine 70 shows all the commands other instances have from DB2 at 601. Next, at 602, the system (i.e., engine 70) suggests a filtered command set based on installation or configuration characteristics, or the user selects a filter and the characteristic of being a primary node of DB2 with another secondary node. Then, at 603, engine 70 shows only those relevant commands that were run from primary nodes of DB2 with another secondary node.

Referring now to FIGS. 4 and 7, another embodiment for providing additional logic to the cloud crowd command information based on relative elapsed time is shown. In this embodiment, engine 70 starts by logging each command and associating a date/time stamp with it. Engine 70 takes this date/time and then derives a relative time, for example, the time since the VM was created. Now each command has an order of operations as well as a relative time executed. This allows cloud users to save off the commands as groups or scripts to be executed quickly against their instances. Statistics may show, for example, that Command Y is usually only executed 10 minutes after Command X, and if executed earlier than that, has a high probability of failing (e.g., starting a service before the previous command finished compiling some code). Therefore, in exemplary embodiments, when the user gets his/her instance 74 and needs to perform an operation, engine 70 can not only recommend the most common commands that others used, but can also recommend a suggested order of commands, wherein the relative time can also be used to augment pure ordered structure.

Consider another example in which 30 minutes after logging in to virtual resource 72 for the first time engine 70 recommends a specific set of commands which are the most commonly executed. The combined commands are then optionally saved into a script and a user can view an option to “run startup script” which could run those command that were commonly run within x minutes of installing a new instance. Similarly, a group of commands might fall into another script for shutdown type commands based on the date/time, etc.

As shown in FIG. 7, engine 70 analyses a relative elapsed time to provide a process 700 for enhanced command management in the cloud computing environment 50 (FIG. 4). In this embodiment, the following steps are performed:

-   -   A. A set of crowd cloud commands are gathered (i.e., step 701).     -   Additional information about the instance is gathered such as:         -   1. Time the instance has been running;         -   2. Order of commands entered; and         -   3. Success and failure responses from executing the             commands;     -   B. Display the most popular or frequent commands executed         against an instance, including the order of the commands entered         (i.e., step 702);     -   C. Group commands and save to one or more scripts based on the         time since the instance was provisioned and the relative order         of the set of commands (i.e., step 703), e.g., those commands         executed within 10 minutes to logging into the instance can be         saved off to a startup script. Or, if the commands tend to be         executed within a certain amount of time or order before an         instance is stopped, those commands can be saved off to a         shutdown script. If other commands tend to be grouped or         immediately executed within a preferred time they could also be         saved as a script to run together;     -   D. Save the command script(s) (i.e., step 704)         -   1. within each instance or, if saved within each             image/instance, the user might type a predefined run command             to initiate the script such as CloudStartup or             CloudShutdown; or         -   2. made available through the cloud management portal; and     -   E. Optionally, instance owners may pick and choose or turn off         commands that appear as part of a script or command grouping to         customize for their environment.

In one embodiment, a statistical probability of success can be assigned to each of the set of suggested commands 95A-N (FIG. 4). For example, the commands can have a statistical probability of success based on commands executed before it. Using this information a user could “ping” a command before actually executing it to determine whether it might be successful. In the case of WebSphere, a user cannot start a server if the node the server is running on hasn't previously been started. Therefore, engine 70 might predict an 80% chance of failure for a user who wishes to check to see if a “startServer.sh” command will complete. The user might see a user experience area within their cloud portal (not shown) listing all of the most popular commands followed by a green/red/orange/yellow icon to suggest the probability it will be successful.

Referring now to FIG. 8, a flow diagram 800 for command value substitutions according to embodiments of the present invention is shown. This embodiment applies to more general command value substitutions, such as a port in an iptables command. The method outlined in flow diagram 800 can apply to any command that contains values, which are variable and can be identified by a flag within the command or by the context of its placement within the command. The method steps for doing so are outlined as follows:

-   -   A. A set of crowd cloud commands are gathered (i.e., step 801);     -   B. Correlate commands which are the same, but may differ in         flags provided and values of the flags provided (i.e., step         802). For example, the following iptables rules where some         values of the flags differ and some of the values are the same:         -   iptables -A INPUT -p tcp -s 10.2.3.4 --dport 80 -j ACCEPT         -   iptables -A INPUT -p tcp -s 10.2.3.7 --dport 443 -j ACCEPT         -   iptables -A INPUT -p tcp -s 10.2.3.8 --dport 8080 -j ACCEPT     -   C. Analyze flags accompanied and their values (i.e., step 803).         For example, the above command iptables command flags can be         analyzed by determining the flag name frequency of flag used         most common value(s) and frequency value of other flags commonly         accompanied by:         -   -A 80% of time INPUT 99% of the time -p, -s, --dport, -j         -   -p 75% of time tcp 85% of time -A, -s, --dport -j         -   -s 70% of time --- varies 95% of time -A, -p, --dport, -j         -   -dport 75% of time 80, 443 40% of time, 40% of time -A, -p,             -s, -j         -   -j 80% of time ACCEPT, DROP 80%, 20% -A, -p, -s, --dport     -   D. Based on the above analysis, the system, when suggesting to         the user to use the “iptables” command, will also suggest flags         to use with it and vary it's suggestion on the values of the         flags based on past statistics (i.e., step 804), e.g.,         -   The system will suggest for the user to use the “-A” flag             with the “INPUT” flag because it is very frequently used in             that fashion, with that value.         -   Because the “-s” flag has a value that almost always varies,             it will prompt the user for the IP address to substitute             here instead of automatically populating it.

Referring now to FIG. 9, consider the following flow diagram 900 in light of the command value substitution shown in FIG. 8. In this case, shutdown, startup and other commands often include a username and/or password entered along with it. Sometimes, this username and password is common among the same type of applications and cloud instances. Other times, they will be unique and private to a user. As part of the overall command suggestion process, engine 70 can also scan for probable usernames and passwords within the history of commands, and decide whether to automatically provide the same username and password for the user as part of the command, or prompt the user to enter in his/her own. In this embodiment, the following steps are performed:

-   -   A. Engine 70 recognizes that a username or password is part of a         command (i.e., step 901) by such methods as:         -   1. The presence of a “-username” or “-password” flag, or             something similar,         -   2. Any other known methods for password identification, such             as an encrypted or “x-ed” out field;     -   B. For the username, compare history of the commands (i.e., step         902) and check to see if the same username tends to be used         consistently (e.g., “db2inst1”) or if it tends to differ by user         (i.e., “johndoe”);     -   C. At 903, it is determined if the same user ID is provided         consistently for that command. If yes, that ID can continue to         be used (i.e., 904);     -   D. If the ID tends to differ, it can instead do either of:         -   a. Prompt the user for the correct ID to use instead (i.e.,             905), or         -   b. Run some analytics to see if there is a pattern or             relation between the ID used in this command and perhaps an             ID of the user known to the system, such as the user's cloud             ID, and use that instead (i.e., 906).     -   E. For the password, security would typically mandate that it         would not be retained in plain text. Therefore, passwords when         stored in the database history of commands can be encrypted, and         in the future, the encrypted values can be compared to see if         they consistently are equal in value. If so, this password can         be utilized in the command as well, Otherwise, the user would be         prompted for a new password.

Referring now to FIG. 10, a method flow 1000 according to an embodiment of the present invention is shown. In step 1001, a set of commands is received for an instance of a virtual resource in the networked computing environment. In step 1002, the set of commands is analyzed to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands. In step 1003, the information is stored in a computer storage device. In step 1004, a request is received from a requester to access at least a subset of the information. In step 1005, the at least a subset of the information from the computer storage device is retrieved to generate a set of suggested commands. In step 1006, the set of suggested commands are refined based on at least one of the following: a level of use of the set of commands, a time since the instance was provisioned, a relative order of the set of commands, a common middleware installed across different images of the instance, newly installed middleware, and an installation topology of the common middleware and the newly installed middleware. In step 1007, the set of suggested commands is provided to the requester.

While shown and described herein as a command identification solution, it is understood that the invention further provides various alternative embodiments. For example, in one embodiment, the invention provides a computer-readable/useable medium that includes computer program code to enable a computer infrastructure to provide command identification functionality as discussed herein. To this extent, the computer-readable/useable medium includes program code that implements each of the various processes of the invention. It is understood that the terms computer-readable medium or computer-useable medium comprise one or more of any type of physical embodiment of the program code. In particular, the computer-readable/useable medium can comprise program code embodied on one or more portable storage articles of manufacture (e.g., a compact disc, a magnetic disk, a tape, etc.), on one or more data storage portions of a computing device, such as memory 28 (FIG. 1) and/or storage system 34 (FIG. 1) (e.g., a fixed disk, a read-only memory, a random access memory, a cache memory, etc.).

In another embodiment, the invention provides a method that performs the process of the invention on a subscription, advertising, and/or fee basis. That is, a service provider, such as a Solution Integrator, could offer to provide command identification functionality. In this case, the service provider can create, maintain, support, etc., a computer infrastructure, such as computer system 12 (FIG. 1) that performs the processes of the invention for one or more consumers. In return, the service provider can receive payment from the consumer(s) under a subscription and/or fee agreement and/or the service provider can receive payment from the sale of advertising content to one or more third parties.

In still another embodiment, the invention provides a computer-implemented method for command identification. In this case, a computer infrastructure, such as computer system 12 (FIG. 1), can be provided and one or more systems for performing the processes of the invention can be obtained (e.g., created, purchased, used, modified, etc.) and deployed to the computer infrastructure. To this extent, the deployment of a system can comprise one or more of: (1) installing program code on a computing device, such as computer system 12 (FIG. 1), from a computer-readable medium; (2) adding one or more computing devices to the computer infrastructure; and (3) incorporating and/or modifying one or more existing systems of the computer infrastructure to enable the computer infrastructure to perform the processes of the invention.

As used herein, it is understood that the terms “program code” and “computer program code” are synonymous and mean any expression, in any language, code, or notation, of a set of instructions intended to cause a computing device having an information processing capability to perform a particular function either directly or after either or both of the following: (a) conversion to another language, code, or notation; and/or (b) reproduction in a different material form. To this extent, program code can be embodied as one or more of: an application/software program, component software/a library of functions, an operating system, a basic device system/driver for a particular computing device, and the like.

A data processing system suitable for storing and/or executing program code can be provided hereunder and can include at least one processor communicatively coupled, directly or indirectly, to memory elements through a system bus. The memory elements can include, but are not limited to, local memory employed during actual execution of the program code, bulk storage, and cache memories that provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. Input/output and/or other external devices (including, but not limited to, keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening device controllers.

Network adapters also may be coupled to the system to enable the data processing system to become coupled to other data processing systems, remote printers, storage devices, and/or the like, through any combination of intervening private or public networks. Illustrative network adapters include, but are not limited to, modems, cable modems, and Ethernet cards.

The foregoing description of various aspects of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed and, obviously, many modifications and variations are possible. Such modifications and variations that may be apparent to a person skilled in the art are intended to be included within the scope of the invention as defined by the accompanying claims. 

What is claimed is:
 1. A computer-implemented method for identifying commands for virtual resource instances in a networked computing environment, comprising: analyzing a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receiving, from a requester, a request to access at least a subset of the information; retrieving the at least a subset of the information from a computer storage device to generate a set of suggested commands; and providing the set of suggested commands to the requester.
 2. The computer-implemented method of claim 1, further comprising refining the at least a subset of the information to generate the set of suggested commands based on at least one of the following: a level of use of the set of commands, a time since the instance was provisioned, a relative order of the set of commands, a common middleware installed across different images of the instance, newly installed middleware, and an installation topology of the common middleware and the newly installed middleware.
 3. The computer-implmented method of claim 2, further comprising: assembling a subset of the set of suggested commands into a script based on the time since the instance was provisioned and the relative order of the set of commands.
 4. The computer-implemented method of claim 1, further comprising: analyzing a set of flags and flag values for the set of commands; and generating a suggested set of flags for the suggested set of commands based on the analyzing.
 5. The computer-implemented method of claim 1, further comprising executing at least one of the set of suggested commands against the instance.
 6. The computer-implemented method of claim 1, further comprising assigning a statistical probability of success to each of the set of suggested commands.
 7. The computer-implemented method of claim 1, the networked computing environment comprising a cloud computing environment and the set of commands comprising a set of commands utilized within a cloud computing environment.
 8. A system for identifying commands for virtual resource instances in a networked computing environment, comprising: a memory medium comprising instructions; a bus coupled to the memory medium; and a processor coupled to the bus that when executing the instructions causes the system to: analyze a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receive, from a requester, a request to access at least a subset of the information; retrieve the at least a subset of the information from a computer storage device to generate a set of suggested commands; and provide the set of suggested commands to the requester.
 9. The system of claim 8, the memory medium further comprising instructions for causing the system to refine the at least a subset of the information to generate the set of suggested commands based on at least one of the following: a level of use of the set of commands, a time since the instance was provisioned, a relative order of the set of commands, a common middleware installed across different images of the instance, newly installed middleware, and an installation topology of the common middleware and the newly installed middleware.
 10. The system of claim 9, the memory medium further comprising instructions for causing the system to assemble a subset of the set of suggested commands into a script based on the time since the instance was provisioned and the relative order of the set of commands.
 11. The system of claim 8, the memory medium further comprising instructions for causing the system to: analyze a set of flags and flag values for the set of commands; and generate a suggested set of flags for the suggested set of commands based on the analyzing.
 12. The system of claim 8, the memory medium further comprising instructions for causing the system to execute at least one of the set of suggested commands against the instance.
 13. The system of claim 8, the memory medium further comprising instructions for causing the system to assign a statistical probability of success to each of the set of suggested commands.
 14. The system of claim 8, the networked computing environment comprising a cloud computing environment and the set of commands comprising a set of commands utilized within a cloud computing environment.
 15. A computer program product for identifying commands for virtual resource instances in a cloud computing environment, the computer program product comprising a computer readable storage media, and program instructions stored on the computer readable storage media, to: analyze a set of commands to identify information pertaining to the set of commands, the information identifying at least one of the following: the set of commands, a date/time stamp associated with the set of commands, relationship artifacts associated with the set of commands, and a set of tags associated with the set of commands; receive, from a requester, a request to access at least a subset of the information; retrieve the at least a subset of the information from a computer storage device to generate a set of suggested commands; and provide the set of suggested commands to the requester.
 16. The computer program product of claim 15, the computer readable storage media further comprising instructions to refine the at least a subset of the information to generate the set of suggested commands based on at least one of the following: a level of use of the set of commands, a time since the instance was provisioned, a relative order of the set of commands, a common middleware installed across different images of the instance, newly installed middleware; and an installation topology of the common middleware and the newly installed middleware.
 17. The computer program product of claim 16, the computer readable storage media further comprising instructions to assemble a subset of the set of suggested commands into a script based on the time since the instance was provisioned and the relative order of the set of commands.
 18. The computer program product of claim 15, the memory medium further comprising instructions for causing the system to: analyze a set of flags and flag values for the set of commands; and generate a suggested set of flags for the suggested set of commands based on the analyzing.
 19. The computer program product of claim 15, the memory medium further comprising instructions for causing the system to assign a statistical probability of success to each of the set of suggested commands.
 20. The computer program product of claim 15, the memory medium further comprising instructions for causing the system to receive at least one command from the requester, the at least one command being identified based on the at least a subset of the information. 